The following is a tabulation of some prior art that presently appears relevant:
U.S. Patent application PublicationsPat. No.Kind CodePubl. DatePatentee0,234,691A12005 Oct. 20Singh et al.5,878,356A11999 Mar. 2Garrot0,101,239A12011 May 5Woodhouse et al.7,058,197B12006 Jun. 6McGuire et al.
It is known in the prior art to employ satellite imagery for monitoring crops such as corn, sugar, wheat, soy, and others. Multispectral satellites measure frequencies of light across the electromagnetic spectrum, beyond what is perceivable to the human eye. With this imagery graphical indicators like the Normalized Difference Vegetation Index (NDVI) can be derived to assess for the presence of live vegetation. Materials can also be classified based on their unique spectral signature as different materials reflect light differently.
The Normalized Difference Vegetation Index (NDVI) is calculated using reflectance values satellites receive in the red and near-infrared (NIR) spectral bands. Chlorophyll, in healthy green-leaf vegetation, absorbs red wavelengths. Conversely, near-infrared wavelengths are reflected by a healthy plant's cellular structure. These two bands construct the NDVI which ranges from negative one to one. NDVI is calculated as follows:NDVI=(NIR−red)/(NIR+red)
Water bodies (void of vegetative matter) have an NDVI value of negative one whereas forests have a positive NDVI value.
Several past patents have proposed using NDVI for crop prediction such as U.S. Pat. No. 0,234,691 (2005). This patent claims that the prediction model can be applied to any vegetable, fruit, grain, nut, legume, etc. ([0041]). For full-sun crops this method may be feasible. Corn is grown in full-sun so the reflectance values collected by a satellite are be reflected directly from the corn plant, the top layer of the given scene. For analysis of shade grown crops however, this method is incompatible as the reflectance values will be those of the outermost canopy layer and not necessarily of the crop under investigation.
Most coffee is shade grown. When applied over a coffee farm U.S. Pat. No. 0,234,691 would not provide any insightful information as to the health of the coffee plants but rather would just provide a loose indicator as to the health of the surrounding forest.
Most cultivated coffee is derived from only two Ethiopian-originating strains: Arabica and Robusta. This lack of genetic diversity within the specie's cultivation is why the crop is notably susceptible to disease. Arabica accounts for 70% of global production and grows best under shade. Robusta is more sun tolerant though is often shade grown. Coffee and its surrounding forest form part of an interdependent agroforestry system. Within a defined spatial environment many species exist. Although coffee may be the farmers focus, it is most certainly not spatially homogeneous like corn, sugar, soy, and other sun grown crops.
U.S. Pat. No. 0,234,691 (2005) extends beyond NDVI by deriving other indicators to gauge a region's growing suitability. The proposed method incorporates rainfall and soil moisture data to derive a yield estimate. These factors however would be largely uncorrelated to the coffee's yield should a disease such as Coffee Rust be effecting the coffee plants below the canopy. This method would be fooled as such a disease may directly increase the NDVI of the canopy layer; should the coffee plants be suffering from Coffee Rust more nutrients and moisture would be available for the surrounding trees making the system appear healthy based on top-level NDVI. Along with coffee, undetectable sub-canopy crops within the agroforestry ecosystem may include: pepper, cacao, and an array of others.
U.S. Pat. No. 7,058,197 (2006) is another NDVI dependent method claiming to be broad enough to monitor virtually any growing vegetation. This method first attempts to cluster regions based on each potential land cover within the area of interest. Next every individual pixel is classified according to its multispectral signature's highest probability likelihood. From each classified pixel an NDVI value is derived and from there a vegetation index value. As this derived vegetation value will change over time and season, the patent claims this method can be used for monitoring crop response zones and temporal cycles such as seasonality. Again this patent fails to account for the heterogeneous nature of an agroforest ecosystem. When coffee is planted usually only the sublayer is cleared and the upper canopy remains unaltered. Thus satellite imagery of a forested coffee farm will be very similar to that of a virgin forest. This method would be unable to classify coffee into a regional cluster. Even if defining areas under coffee cultivation into regional clusters was possible, the vegetation index would only be reflective of the tallest vegetative matter (most commonly trees) in the agroforestry system—not an effective means of monitoring the coffee below. For the same reasons. U.S. Pat. No. 0,214,984 (2009) also fails as a means for monitoring shade grown crops.
Even aerial based methods of remote sensing (such as UAVs and airplanes) are unable to monitor sub-canopy crops. U.S. Pat. No. 5,878,356 (1999) proposes collecting visual and infrared imagery with an Unmanned Aerial Vehicle for resource monitoring. This patent claims that with such imagery an Indo-Jackson Crop Water Stress Index can be derived to measure foliage temperature. With foliage temperature one can derive crop influencing factors such as soil moisture content, soil water matrix potential, and photosynthesis. While canopy-level reflectance readings may indicate properties such as soil moisture, this method fails to assess coffee health and disease variables such as Coffee Rust Disease and the Coffee Bean Borer. Both have a tremendous impact on coffee harvests globally. This patent relies too heavily on canopy derived indices which don't reflect the health of an entire agroforestry ecosystem. This method would also not be feasible for monitoring coffee on a large scale given the limitations of UAVs. This patent claims to addresses the costly nature of acquiring satellite imagery. Fortunately, much satellite imagery is now freely available for public use.
U.S. Pat. No. 0,101,239 (2011) combines multispectral and LIDAR imagery. LIDAR penetrates the vegetation to reflect a forest's true ground layer. LIDAR combined with reflectance readings from a forest's canopy can be combined to calculate biomass with a simple volumetric calculation.
A large forest biomass however may not correlate to a great expanse of coffee plants within the system and may correlate very little with the actual yield of these plants. Using this method a very tall and thick canopy layer would suggest a large biomass. As measurements are only taken at ground and canopy levels, there is no way to quantify medium height sub-canopy vegetation such as coffee. Coffee's biomass as a portion of an agroforestry system is very variable. New coffee may be planted under a very tall forest yielding a large biomass reading for the scene despite such small and unproductive coffee plants.